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| import gradio as gr | |
| from portiloop.src.demo.offline import run_offline | |
| def on_upload_file(file): | |
| # Check if file extension is .xdf | |
| if file.name.split(".")[-1] != "xdf": | |
| raise gr.Error("Please upload a .xdf file.") | |
| else: | |
| return file.name | |
| def main(): | |
| with gr.Blocks(title="Portiloop") as demo: | |
| gr.Markdown("# Portiloop Demo") | |
| gr.Markdown("This Demo takes as input an XDF file coming from the Portiloop EEG device and allows you to convert it to CSV and perform the following actions:: \n * Filter the data offline \n * Perform offline spindle detection using Wamsley or Lacourse. \n * Simulate the Portiloop online filtering and spindle detection with different parameters.") | |
| gr.Markdown("Upload your XDF file and click **Run Inference** to start the processing...") | |
| with gr.Row(): | |
| xdf_file_button = gr.UploadButton(label="Click to Upload", type="file", file_count="single") | |
| xdf_file_static = gr.File(label="XDF File", type='file', interactive=False) | |
| xdf_file_button.upload(on_upload_file, xdf_file_button, xdf_file_static) | |
| # Make a checkbox group for the options | |
| detect_filter = gr.CheckboxGroup(['Offline Filtering', 'Lacourse Detection', 'Wamsley Detection', 'Online Filtering', 'Online Detection'], type='index', label="Filtering/Detection options") | |
| # Threshold value | |
| threshold = gr.Slider(0, 1, value=0.82, step=0.01, label="Threshold", interactive=True) | |
| # Detection Channel | |
| detect_channel = gr.Dropdown(choices=["1", "2", "3", "4", "5", "6", "7", "8"], value="2", label="Detection Channel in XDF recording", interactive=True) | |
| # Frequency | |
| freq = gr.Dropdown(choices=["100", "200", "250", "256", "500", "512", "1000", "1024"], value="250", label="Sampling Frequency (Hz)", interactive=True) | |
| output_array = gr.File(label="Output CSV File") | |
| run_inference = gr.Button(value="Run Inference") | |
| run_inference.click( | |
| fn=run_offline, | |
| inputs=[ | |
| xdf_file_static, | |
| detect_filter, | |
| threshold, | |
| detect_channel, | |
| freq], | |
| outputs=[output_array]) | |
| demo.queue() | |
| demo.launch(share=True) | |
| if __name__ == "__main__": | |
| main() | |